So you are building a software application. One of the first problems to be solved is how to store your data. Which database will you choose?

A Database Management System or DBMS is a software that communicates with the database itself, applications, and user interfaces to obtain data and parse it. The DBMS also contains the key instruments to govern the database.

For our comparison, we’ve picked 9 most commonly used database management systems: MySQL, MariaDB, Oracle, PostgreSQL, MSSQL, MongoDB, Redis, Cassandra, and Elasticsearch. Concentrating on their business-related benefits and the challenges, we’ll also outline the best use cases for each.

Relational vs Non-Relational: What’s the difference?

Basically, there are two types of DBMSs: relational and non-relational, also referred to as SQL and NoSQL. They differ in terms of data retrieval, distribution, and processing.

Relational. Since a Structured Query Language is the core of these systems, this type is also called SQL. In relational DBMSs, the data appears as tables of rows and columns with a strict structure and clear dependencies.

Due to the integrated structure and data storage system, SQL databases don’t require much engineering effort to make them well-protected. They are a good choice for building and supporting complex software solutions, where any interaction has a range of consequences. One of the SQL fundamentals is ACID compliance (Atomicity, Consistency, Isolation, Durability). The ACID-compliance is a preferred option if you build, for instance, eCommerce or financial applications, where database integrity is critical.

However, scalability can be a challenge with SQL databases. Scaling an SQL database between multiple servers (horizontal scaling) takes additional engineering efforts. Instead, SQL databases are usually scaled vertically, i.e. by adding more computing power to a server. Here, we’ll discuss several SQL databases:

MySQL

MariaDB

Oracle

PostgreSQL

MSSQL

Non-relational. As these databases aren’t limited to a table structure, they are called NoSQL. This type of database management system is considered document-oriented. Non-structured data such as articles, photos, videos, and others are collected in a single document. Data is simple to query but isn’t always classified into rows and columns as in a relational database. Non-relational or NoSQL databases are usually horizontally scaled by adding servers.

Since NoSQL databases allow for reserving various data types together and scaling it by growing around multiple servers, their never-decreasing popularity is understandable. Also, building an MVP it’s a great option for startups with sprint-based Agile development. NoSQL requires no pre-deployment preparations, making quick, time-lag-free updates to the data structure easier.

We’ll discuss such NoSQL databases as:

MongoDB

Redis

Cassandra

Elasticsearch

So, what are the most commonly used database systems in SQL and NoSQL? What are their main advantages and disadvantages, and how should businesses use them? Let’s take a deeper look.

Database management systems in a nutshell

MySQL

This is one of the most popular relational database systems. Originally an open-source solution, MySQL now is owned by Oracle Corporation. Today, MySQL is a pillar of LAMP application software. That means it’s a part of Linux, Apache, MySQL, and Perl/PHP/Python stack. Having C and C++ under the hood, MySQL works well with such system platforms as Windows, Linux, MacOS, IRIX, and others.

Pros of MySQL

Free installation. The community edition of MySQL is free to download. With a basic set of tools for individual use, MySQL community edition is a good option to begin with. Of course, there are other, prepaid options for Enterprise or Cluster purposes with richer functionality. Nevertheless, if your company is too small to pay for one of them, the free-to-download model is the most suitable for a fresh start.

Simple syntax and mild complexity. MySQL’s structure and style are very plain. Developers even consider MySQL a database with a human-like language. As MySQL is often used in tandem with PHP programming language. Because they share a gentle learning curve, you won’t need to hire a skilled developer to manage your database. Also, MySQL is easy to use. For instance, most of the tasks can be executed right in the command line, reducing development steps.

Cloud-compatible. Business-oriented by nature and originally developed for the web, MySQL is supported by the most popular cloud providers. It’s available on such leading platforms as Amazon, Microsoft, and others. This makes MySQL even more attractive and gives businesses using it room for growth.

Cons of MySQL

Scalability challenges. MySQL was not built with scalability in mind, which is inherent in its code. In theory, you can scale MySQL, but it will need more engineering effort as compared to any of the NoSQL databases. So, if you expect one day your database will increase substantially, keep this limitation in mind or choose another DBMS option.

Partial open source. Although MySQL has the open-source part, it’s mostly under Oracle’s license. This limits the MySQL community in terms of improving the DBMS. Why do you care? Because when you have completely open-source support, you expect many problem-specific implementations and community assistance. This is not the case when the software belongs to corporate owners and you’ll have to pay for support.

Limited compliance with SQL standards. Structured Query Language has specific standards. MySQL doesn’t completely follow them, i.e. MySQL provides no support for some standard SQL features. On the other hand, MySQL has some extensions and distinct features that don’t match the Structured Query Language standards. It’s not a big deal for small web applications. The issues may appear when you have to shift to other databases, which is likely to happen when your business starts growing.

Use cases

Small web-based solutions. MySQL database system is the best option when you’re designing a small, web-based solution with a small volume of data. For example, when building a local eCommerce store, MySQL may come in handy.

OLAP/OLTP systems. This is one of the best use cases for a MySQL database, as OLAP/OLTP don’t require complex queries and large volumes of data. Also, consider applying MySQL for the same reason if you’re building a business intelligence tool.

MariaDB

MariaDB, an open-source fork from MySQL, has commercial support. It works under a GNU General Public License and has similar commands, APIs, and libraries as MySQL.

Pros of MariaDB

Encryption. For MariaDB, open source doesn’t mean insecure. In addition to internal security and password check, MariaDB provides such features as PAM and LDAP authentication, Kerberos, and user roles. In combination with encrypted tablespaces, tables, and logs, it creates a robust protective layer for data. Above that, MariaDB publishes the related releases on each security update, keeping the security patches totally transparent.

Broad functionality. MariaDB has introduced a lot of new features in the last few years. For instance, GIS support suggests smooth coordinates storage and location data query. Dynamic columns allow a single DBMS to provide both SQL and NoSQL data handling for different needs. You also can extend its functionality with plugins that are available at MySQL via 3rd parties only. MariaDB is shipped with storage engines for NoSQL backend, legacy databases migration tools, sharding options, and many more.

High performance. Although MariaDB originates from the MySQL engine, it got very far in terms of performance. Extensive optimization features improve thread pool management and data processing. Thus, when rows from the table are deleted, the operating system immediately accesses the free space, eliminating gaps in the tablespace. On top of that, the database management system suggests engine-independent table statistics. This feature enhances the optimizer’s performance, accelerates query processing, and helps customize data analysis.

Cons of MariaDB

Still growing community. Albeit MariaDB has a substantial open-source contribution, its community is yet to grow. Since this database management system was established not so long ago, the number of professionals is relatively small.

Gaps between MySQL and MariaDB update versions. Though the MariaDB team is constantly merging its code with that of MySQL, it’s already not that simple to keep them in line. Given currently existing differences between MariaDB 10.4 and MySQL 8.0, further deviations are yet to come. Additionally, MySQL engineers introduce some native features to the code that are only available to commercial MySQL users. This can create compatibility issues or migration problems from MariaDB back to MySQL.

Use cases

Since MariaDB is close to MySQL, it can be used to work with the same types of web-based applications. Additionally, you get extended location data storage, higher performance, and improved scalability.

Oracle

Oracle is a relational database management system created and run by the Oracle Corporation. Currently, it supports multiple data models like document, graph, relational, and key-value within the single database. In its latest releases, it refocused on cloud computing. Oracle database engine licensing is fully proprietary, with both free and paid options available.

Strong tech support and documentation. Oracle ensures decent customer support and provides comprehensive tech documentation across multiple resources. So, you’ll likely find solutions to any issues that appear. You may also expect some community support.

Large capacity. Oracle’s multi-model solution allows for accommodating and processing a vast amount of data. Thanks to the recently released multi-tenancy feature, the database architecture now simplifies packing many databases and manage them smoothly. In combination with in-memory data processing capabilities, it creates a strong engine for synchronous data processing.

Cons of Oracle

High cost. Though Oracle 12c RDBMS has free editions, they are very limited in terms of functionality. Standard Edition, which doesn’t include all available features, costs $17,500 per unit. The Enterprise Edition is over $47,000 per unit.

Resource-consuming. Oracle database needs powerful infrastructure. Not only does installation require a lot of disk space, you’ll also have to consider constant hardware updates if you deploy it on premises.

Hard learning curve. Oracle database is not a system to start using right away. It’s better to have certified Oracle DB engineers to run it. Oracle’s documentation, while covering many issues, can sometimes be overwhelming and even confusing. So, to install and run an Oracle database, you’ll have to consider hiring dedicated experts.

Use cases

Given all those perks and pitfalls, you can consider Oracle RDMS as a reasonable solution for online OLTP, data warehousing, and even mixed (OLTP and DW) database application. If you have a billion records to hold and manage – and sufficient budget to support it – Oracle hybrid cloud software is a good option to choose.

PostgreSQL

This database management system shares its popularity with MySQL. This is an object-relational DBMS where user-defined objects and table approach are combined to build more complex data structures. Besides that, PostgreSQL has a lot of similarities with MySQL. It’s aimed at strengthening the standards of compliance and extensibility. Consequently, it can process any workload, for both single-machine products and complex applications. Owned and developed by PostgreSQL Global Development Group, it still remains a completely open source. This DBMS is available for use with such platform systems as Microsoft, iOS, Android, and many more.

Pros of Postgre

Scalable. Vertical scalability is a hallmark of PostgreSQL, unlike MySQL DBMS. Considering that almost any custom software solution tends to grow, resulting in database extension, this particular option certainly supports business growth and development.

Support for custom data types. PostgreSQL natively supports a large number of data types by default, such as JSON, XML, H-Store, and others. PostgreSQL takes advantage of it, being one of the few relational databases with strong support for NoSQL features. Additionally, it allows users to define their own data types. As your software business model may need different types of databases throughout its existence for better performance or application comprehensiveness, this option brings improved flexibility to the table.

Easily-integrated third-party tools. PostgreSQL database management system has the strong support of additional tools, both free and commercial. The scope of these includes extensions to improve many aspects. For example, ClusterControl provides impressive assistance at managing, monitoring, and scaling SQL and NoSQL open source databases. To make data comparison and synchronization more effective, consider using DB Data Difftective. In case you’re going to scale up your data to heavy workloads, pgBackRest backup and restore system will be a nice option to choose.

Open-source and community-driven. Postgres is completely open-source and supported by its community, which strengthens it as a complete ecosystem. Additionally, developers can always expect free and prompt community assistance.

Cons of Postgre

Inconsistent documentation. While PostgreSQL has a large community and provides strong support for its participants, the documentation still lacks consistency and completeness. As the PostgreSQL community is rather distributed, the documentation doesn’t follow equal standards for all Postgre features.

Lack of reporting and auditing instruments. A significant shortcoming of PostgreSQL is the absence of revising tools that would show the current condition of a database. You have to continuously check if something goes wrong. There’s always a risk that DB engineers will notice a failure too late.

Use cases

Due to complicated queries and a wide choice of custom interfaces accomplished with predefined functions, PostgreSQL is a perfect match for data analysis and warehousing. If you are building a database automation tool, PostgreSQL is the best fit for it due to its strong analytical capabilities, ACID-compliance, and powerful SQL engine. All in one, it significantly accelerates the processing of vast amounts of data. This DBMS is popular with financial institutions and telecommunication systems.

MSSQL

As a completely commercial tool, Microsoft SQL Server is one of the most popular relational DBMS, in addition to MySQL, PostgreSQL, and Oracle. It copes well with effective storing, changing, and managing relational data. To interact with SQL Server databases, DB engineers usually utilize the Transact-SQL (T-SQL) language, which is an extension of the SQL standard.

Pros of MSSQL

Variety of versions. Microsoft SQL Server provides a wide choice of different options with diverse functionalities. For instance, the Express edition with a free database offers entry-level tooling, the perfect match for learning and building desktop or small server data-driven applications. The Developers option allows for building and testing applications including some enterprise functionalities, but without a production server license. For bigger projects, there are also Web, Standard, and Enterprise editions, with a varying extent of administrative capabilities and service levels.

Rich documentation and community assistance. With Microsoft SQL Server aimed at comprehensive database maintenance, the full online documentation also reflects this concept. The consequently structured guidelines, numerous whitepapers, and demos give a full picture on the MSSQL data system. Also, Microsoft Premier provides access to dedicated Microsoft community support, which is an advantage when a DB engineer needs assistance.

Cloud database support. Being a part of the consistent Microsoft ecosystem, MSSQL can be integrated with Microsoft cloud, Azure SQL Database, or SQL Server on Azure Virtual Machines. The solutions allow shifting database administration to the cloud if your business software database becomes really overwhelming and hard to administer.

Cons of MSSQL

Cost-consuming. Being mostly used at enterprise scale, MSSQL Server remains one of the most expensive solutions. Speaking of numbers, the Enterprise edition currently costs over $14, 000 per core, sold as 2 core packs.

Unclear and floating license conditions. Another issue is the ever-changing licensing process. The pricing strategy itself is hard to understand and the elements included in a particular edition are floating, tending to shift from one to another.

Complicated tuning process. For those beginners who have to operate heavy data sets, working with query optimization and performance tuning may be problematic. As the process is not so obvious, it can create substantial bottlenecks early on.

Use cases

MSSQL Server is a reasonable option for companies with other Microsoft product subscriptions. As Microsoft creates a sustainable ecosystem with well-integrated services, the MSSQL here with its access to cloud and powerful data retrieval tools comes in handy.

MongoDB

A free, open-source, non-relational DBMS, MongoDB also includes a commercial version. Although MongoDB wasn’t initially intended for structured data processing, it can be employed for applications that use both structured and unstructured data. In MongoDB, databases are connected to applications via database drivers. They are widely available within the database management system. Multiple types of data are processed simultaneously and use the internal cache for this purpose.

Pros of MongoDB

Simple data access, storage, input, and retrieval. One of the benefits of MongoDB derived from its NoSQL nature is the fast and easy data operation. That is to say, data can be entered, stored, and withdrawn from the database quickly and without any additional confirmation. As any other non-relational database, it places emphasis on RAM usage, so the records can be manipulated really fast and without any consequences to data integrity.

Easy compatibility with other data models. MongoDB is easily combined with different database management systems, both SQL and NoSQL types. Besides that, it has pluggable storage engine APIs. To make a long story short, this option allows third parties to build their own data storage engines for MongoDB. From a commercial point of view, it creates extra value for business software.

Horizontally scalable solution. Scalability – where data is spread out across a distributed network of manageable servers – is a facet of MongoDB’s fundamental nature. It becomes even more important for enterprises operating big data applications. Additionally, the database can allocate data across a cluster of machines. How can that help you? The data is distributed faster and equally, free of bulkiness. As it leads to faster data processing, the application performance is accelerated too.

Cons of MongoDB

Extensive memory consumption. The denormalization process. when previously normalized data in a database is grouped to increase performance, usually results in high memory consumption. Also, this DBMS keeps in memory all key names for each value pair. Beyond that, because there is no support for joins, Mongo databases have data oversupply, resulting in big memory waste and lower application performance.

Data insecurity. With a focus on fast data operation, MongoDB, like any other NoSQL DBMS, lacks data security. As user authentication isn’t a default Mongo option, and higher protection is available with a commercial edition only, you can’t consider it totally secure. Additionally, there are constant MongoDB update releases, with no guarantee that all amendments or data changes will work as they did before. Keep in mind that all manipulations should be formed around these updates, being covered with additional tests.

Complicated process to interpret into other query languages. As MongoDB wasn’t initially developed to deal with relational data models, the performance may slow down in these cases. Besides, the translation of SQL to MongoDB queries takes additional action to use the engine, which may delay the development and deployment.

Use cases

MongoDB works best in real-time data integration and database scalability. For instance, it’s the right option for product catalogs due to its capacity to stock a multiplicity of objects with various attribute collections. Also, consider here analytic platforms, as MongoDB’s speed provides dynamic performance that can help track the user’s behavior in real time.

Redis

An open-source, NoSQL, in-memory data structure store, Redis can also be used as a cache. Instead of documents, it uses key-value pairs. Its distinct feature is that there are several options for data structuring, such as lists, sets, and hashes.

Allowing for data replication and supporting transactions, Redis executes commands in a queue instead of setting it one at a time.

Pros of Redis

Rapid solution. Due to its replication and transaction features, Redis processes the data really fast. The absence of dependencies and in-memory data store type makes Redis a worthy competitor even among simple SQL alternatives.

Massive data processing. From the data perception and refining perspective, Redis can be considered a colossus. It can easily upload up to 1GB of data for one entry. Add built-in data caching and you get a powerhouse data machine.

Cons of Redis

Requires dataset to fit into memory. Total reliance and dependency on the application memory is a real drawback. That is to say, your database will crash if its size exceeds the size of available memory.

No support for query language or joins. Regarding compatibility with other dataset types, Redis lags behind. Given that at some time your business may need scaling and using other data formats, having rapid entries as a single option leaves this issue open.

Use cases

Redis basically has a few different directions to work with. And the first of them is IoT applications. Here, heavy data from IoT devices can be transferred to Redis to process these records before keeping them in any steady data storage. Also, Redis is a perfect option for microservice architectures with scalable cloud hosting. As data here doesn’t have to be long term persistent, Redis seems a reasonable decision.

Cassandra

Cassandra is a decentralized system developed by Apache. Cassandra is a free DBMS whose strength is in its multi-replication and multi-deployment features. These peculiarities allow for numerous query copying and deploying all of them at the same time. Being rapidly scalable, Cassandra allows for managing large data volumes by replicating it into multiple nodes. It eliminates the problem of database crash – if some of the nodes fail at any time, it’s replaced immediately, and the system keeps working as long as at least one single node is safe.

Cassandra uses its own query language, CQL. In its syntax, it’s very similar to SQL but doesn’t apply joins, replacing them with so-called column families. And the second difference is that not all columns in a table are stored for subqueries. Some of them are used as clustering columns, where adjacent data is put next to each other for fast retrieval. Why does that matter? It provides faster querying from massive datasets, accelerating data processing.

Pros of Cassandra

Data security. Due to its master node replication feature, Cassandra stays failure tolerant. It means that DB engineers can feel confident about data safety unless master nodes fail all at the same time. As long as it’s extremely unlikely, the database and the application built on it will stay sound and secure.

Flexibility and on-hand amendments. Casandra’s simple syntax has the best of SQL and NoSQL. In addition to scalability, it largely contributes to dataset flexibility. Cassandra collects data on the go, and data retrieval shares the same simplicity, despite dataset size. This allows enlarging the database to the fullest extent.

Cons of Cassandra

Slow reading. As Cassandra was initially designed for fast writing, its weakness lies in its incapacity for fast reading. One of the reasons for it is that there are no bottlenecks for information sent, so it needs more time to process.

Requires additional resources. As Cassandra processes multiple layers of data simultaneously, it demands enough power to do it, resulting in the JVM usage. This means additional investment in both software and hardware. If this is the first time a company faces such a necessity and is not sure about the resources, then maybe it should consider other database systems.

Use cases

Thanks to even data distribution, Cassandra is relevant in applications where large volumes of information are processed. For instance, it’s a great choice for data centers. Also, Cassandra fits well with real-time analytics, as it allows linear scaling and data increase in real time. You may also consider it for applications with constant data streaming like weather apps. Another option is using it as a DBMS for an eCommerce store, as it allows for storing purchase history and other transactions. Add here feasibility to track such data types as order status and packages, and you’ll get the full solution with eCommerce delivery integration.

Elasticsearch

Elasticsearch is a NoSQL, document-oriented database management system having a full-text search engine in its heart. Built on the Apache Lucene library, it stores data as a JSON file, supports RESTful APIs, and uses a powerful analytical engine for faster data retrieval. Being open-source software, it includes both free and paid editions.

Pros of Elasticsearch

Scalable architecture. One of Elasticsearch’s peculiarities is its robust distributed architecture. Its key structure options, such as clustering, indexing, sharding, and many more, provide extensive horizontal scaling, which allows for accommodating terabytes of records with further automation. The architecture’s abstraction levels streamline system management on both individual and aggregate levels.

Fast data processing. Due to the distributed data structure and built-in parallelization, the Elasticsearch DB shows excellent performance results. Even when executing a complex data query, it generates lightning search result response. This is partly available due to documents being maintained close to relevant metadata in the index, which makes them fast to find.

Cons of Elasticsearch

Lack of multi-language support. When handling request or response data, Elasticsearch DBMS lags behind. Though it’s perfectly combined with Cassandra DB to complement database performance, other languages and formats are not available for it. In these terms, it only supports JSON document format.

Limited consistent health check tools. When something goes wrong, as it may at any stage, Elasticsearch can only show status as “yellow” or “red.” Simply put, it has no reporting tools. Though issues are usually like memory threshold or disk capacity, DBA engineers complain about the situation.

Use cases

Due to its NoSQL distributed nature and flexible data models, Elasticsearch is a great tool for eCommerce products with huge databases that tend to use search engines. It’s very helpful when creating or updating a customer’s profile in terms of workload that real-time engagement usually demands.

To wrap up

There’s a large number of database management systems out there. Each of them is good in its own way, having some drawbacks as well. Of course, we haven’t covered even a third of them, but we tried to compare those commonly used for both small web applications and big data warehousing systems.

So, how do you choose the right one for your own software application?

If you are just starting a local eCommerce business, MySQL is a sensible jumping-off point that will also work well for web-based BI tools and OLTP systems.

In case you are striving to build the eCommerce giant with a complete buyer journey for your customer, no doubt about it, use Casandra. To complement it with a powerful search engine, you may also attach the Elasticsearch database solution.

Speaking of Cassandra, it’s also the best option for data centers and real-time analytics with oceanic volumes of data.

When speaking of analytic tools without multiple data layers, opt for MongoDB. It also performs well for product catalogs.

Following up the scope of data warehousing applications, MSSQL is also worth a mention, especially for companies with a number of other Microsoft subscriptions.

In terms of building an OLTP solution and data warehousing applications, Oracle is a good choice as well.

IoT application and microservice architecture that tend to scale its data hosting will summarize our list of best use cases with Redis.

Sure, there are more database systems to consider. It all depends on your business model and your business needs.